Optimal 6E design of an integrated solar energy-driven polygeneration and CO2 capture system: A machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{KHANI:2023:tsep,

• author = "Nastaran Khani and Mohammad H. {Khoshgoftar Manesh} and Viviani C. Onishi",
• title = "Optimal 6E design of an integrated solar energy-driven polygeneration and {CO2} capture system: A machine learning approach",
• journal = "Thermal Science and Engineering Progress",
• volume = "38",
• pages = "101669",
• year = "2023",
• ISSN = "2451-9049",
• DOI = "doi:10.1016/j.tsep.2023.101669",
• URL = "https://www.sciencedirect.com/science/article/pii/S2451904923000227",
• keywords = "genetic algorithms, genetic programming, 6E Analyses, Sensitivity Analysis, Multi-objective Optimization, Solar Energy, Dynamic analysis, Humidification-Dehumidification (HDH)",
• abstract = "Renewable energy-driven decentralized polygeneration systems herald great potential in tackling climate change issues and promoting sustainable development. In this light, this study introduces a new machine learning-based multi-objective optimization approach of an integrated solar energy-driven polygeneration and CO2 capture system for meeting a greenhouse's power, freshwater, and CO2 demands. The integrated solar-assisted polygeneration system comprises a 486-kW gas turbine, two steam turbines, two organic Rankine cycles, a humidification-dehumidification desalination unit to recover waste heat while producing freshwater, and a post-combustion CO2 capture unit. The proposed system is mathematically modelled and evaluated via a dynamic simulation approach implemented in MATLAB software. Moreover, sensitivity analysis is conducted to identify the most influential decision variables on the system performance. The machine learning-based multi-objective optimization strategy combines Genetic Programming (GP) and Artificial Neural Networks (ANN) to minimize total costs, environmental impacts, and economic and environmental emergy rates whilst maximizing the system exergy efficiency and freshwater production. Finally, the system performance is further investigated through comprehensive Energy, Exergy, Exergoeconomic, Exergoenvironmental, Emergoeconomic, and Emergoenvironmental (6E) analyses. The three-objective optimization of the integrated system reduces total costs, environmental impacts, and monthly environmental emergy rate by 11.4percent, 34.31percent and 6.38percent, respectively. Furthermore, reductions up to 56.81percent, 50.19percent and 77.07percent, respectively, are obtained for the previous indicators by the four-objective optimization model. Hence, the proposed multi-objective optimization methodology represents a valuable tool for decision-makers in implementing more cost-effective and environment-friendly solar-assisted integrated polygeneration and CO2 capture systems",

}

Genetic Programming entries for Nastaran Khani Mohammad Hasan Khoshgoftar Manesh Viviani C Onishi

Citations